Debiased inverse propensity score weighting for estimation of average treatment effects with high-dimensional confounders
高维混杂下平均处理效应的去偏逆倾向得分加权
We consider estimation of average treatment effects given observational data with high-dimensional pretreatment variables. Existing methods for this problem typically assume some form of sparsity for the regression functions. In this work, we introduce a debiased inverse propensity score weighting (DIPW) scheme for average treatment effect estimation that delivers n-consistent estimates when the propensity score follows a sparse logistic regression model; the outcome…